The present disclosure relates generally to the field of process control systems. More particularly, the present disclosure relates to the control of appliances.
Optimization and modeling algorithms are used to optimize and model various environments. Some systems utilize biologically inspired algorithms (e.g., genetic algorithms, particle swarm intelligence, evolutionary strategies, etc.) to perform optimization and modeling due to their ability to properly learn and optimize variant environments rapidly. Genetic algorithms may be relatively slow and ineffective at handling environments which change during optimization. Such genetic algorithms may need to be restarted to effectively handle the changes upon detection of an environmental change, which may result in poor performance of the algorithm. Biologically inspired algorithms also are not equipped to maintain memory of previously encountered environments and must be restarted from scratch upon significant environmental change detection. This may result in poor performance and/or abandonment of the methodology for optimization and modeling.
Hot water heaters cycle a heating device through employment of a control using a hysteresis loop that activates heating at a set low temperature point and stops heating at a set high temperature point. This causes the appliance to cycle as a function of the internal tank water temperature measured at the point of the temperature sensor without regard to usage pattern, demand, and/or the current price of energy. Other household or industrial appliances designed to heat or cool such as boilers, chillers, and furnaces operate in a similar manner. Excess energy may be consumed to heat the water to the set high temperature point when there is little or no demand. Additionally, the water may be heated without regard to energy price fluctuations.